Triple

T30231417
Position Surface form Disambiguated ID Type / Status
Subject Fujiwara-kyō site E768638 entity
Predicate associatedWith P37 FINISHED
Object Emperor Genmei
Emperor Genmei was an early 8th-century Japanese empress who oversaw the transition of the imperial capital and played a key role in consolidating the Nara period state.
E1911862 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Emperor Genmei | Statement: [Fujiwara-kyō site, associatedWith, Emperor Genmei]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Emperor Genmei
Triple: [Fujiwara-kyō site, associatedWith, Emperor Genmei]
Generated description
Emperor Genmei was an early 8th-century Japanese empress who oversaw the transition of the imperial capital and played a key role in consolidating the Nara period state.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f2248108208190be60bf1af343ce70 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f680261d0c8190968490bfab44f96c completed May 2, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2789277b4081909483f31a40503794 completed June 9, 2026, 3:31 a.m.
NEDg Description generation batch_6a27899e1e608190ab47ff85d1a25738 completed June 9, 2026, 3:33 a.m.
NED2 Entity disambiguation (via description) batch_6a2789f2f6f0819099891123a61feb50 completed June 9, 2026, 3:35 a.m.
Created at: April 29, 2026, 7:36 p.m.